DE602005020649D1 - BIOPOTENTIAL SIGNALFORM DATA-FUSION ANALYSIS AND CLASSIFICATION PROCEDURES - Google Patents

BIOPOTENTIAL SIGNALFORM DATA-FUSION ANALYSIS AND CLASSIFICATION PROCEDURES

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Publication number
DE602005020649D1
DE602005020649D1 DE602005020649T DE602005020649T DE602005020649D1 DE 602005020649 D1 DE602005020649 D1 DE 602005020649D1 DE 602005020649 T DE602005020649 T DE 602005020649T DE 602005020649 T DE602005020649 T DE 602005020649T DE 602005020649 D1 DE602005020649 D1 DE 602005020649D1
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DE
Germany
Prior art keywords
classification
biopotential
classifiers
univariate
channels
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
DE602005020649T
Other languages
German (de)
Inventor
Lalitmore Gupta
Kalford C Fadem
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Southern Illinois University System
Neuronetrix Solutions LLC
Southern Illinois University Carbondale
Original Assignee
Southern Illinois University System
Neuronetrix Solutions LLC
Southern Illinois University Carbondale
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Southern Illinois University System, Neuronetrix Solutions LLC, Southern Illinois University Carbondale filed Critical Southern Illinois University System
Publication of DE602005020649D1 publication Critical patent/DE602005020649D1/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/377—Electroencephalography [EEG] using evoked responses
    • A61B5/378—Visual stimuli
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/377—Electroencephalography [EEG] using evoked responses
    • A61B5/38—Acoustic or auditory stimuli
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G06F18/254—Fusion techniques of classification results, e.g. of results related to same input data
    • G06F18/256—Fusion techniques of classification results, e.g. of results related to same input data of results relating to different input data, e.g. multimodal recognition
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Abstract

Biopotential waveforms such as ERPs, EEGs, ECGs, or EMGs are classified accurately by dynamically fusing classification information from multiple electrodes, tests, or other data sources. These different data sources or "channels" are ranked at different time instants according to their respective univariate classification accuracies. Channel rankings are determined during training phase in which classification accuracy of each channel at each time-instant is determined. Classifiers are simple univariate classifiers which only require univariate parameter estimation. Using classification information, a rule is formulated to dynamically select different channels at different time-instants during testing phase. Independent decisions of selected channels at different time instants are fused into a decision fusion vector. Resulting decision fusion vector is optimally classified using a discrete Bayes classifier. Finally, dynamic decision fusion system provides high classification accuracies, is quite flexible in operation, and overcomes major limitations of classifiers applied currently in biopotential waveform studies and clinical applications.
DE602005020649T 2004-08-30 2005-08-30 BIOPOTENTIAL SIGNALFORM DATA-FUSION ANALYSIS AND CLASSIFICATION PROCEDURES Active DE602005020649D1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US60563004P 2004-08-30 2004-08-30
PCT/US2005/030662 WO2006026548A1 (en) 2004-08-30 2005-08-30 Biopotential waveform data fusion analysis and classification method

Publications (1)

Publication Number Publication Date
DE602005020649D1 true DE602005020649D1 (en) 2010-05-27

Family

ID=35453371

Family Applications (1)

Application Number Title Priority Date Filing Date
DE602005020649T Active DE602005020649D1 (en) 2004-08-30 2005-08-30 BIOPOTENTIAL SIGNALFORM DATA-FUSION ANALYSIS AND CLASSIFICATION PROCEDURES

Country Status (6)

Country Link
EP (1) EP1789907B1 (en)
JP (1) JP2008517636A (en)
AT (1) ATE464616T1 (en)
AU (1) AU2005279954B2 (en)
DE (1) DE602005020649D1 (en)
WO (1) WO2006026548A1 (en)

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WO2007022524A2 (en) 2005-08-19 2007-02-22 Neuronetrix, Inc. Controller for neuromuscular testing
KR100866215B1 (en) * 2006-12-20 2008-10-30 삼성전자주식회사 Method for operating a terminal using brain wave and appartus thereof
US8463371B2 (en) * 2007-02-09 2013-06-11 Agency For Science, Technology And Research System and method for processing brain signals in a BCI system
WO2009126997A1 (en) * 2008-04-18 2009-10-22 Commonwealth Scientific And Industrial Research Organisation Hierarchical activity classification method and apparatus
JPWO2010029832A1 (en) * 2008-09-10 2012-02-02 株式会社日立メディコ Biological light measurement device
AU2011299099B2 (en) * 2010-09-10 2015-02-26 Neuronetrix Solutions, Llc Biomarker fusion system and method
EP2699158A4 (en) 2011-04-20 2014-10-15 Brigham & Womens Hospital System and method for acquiring patient physiological information during an mri scan
CN103190904B (en) * 2013-04-03 2014-11-05 山东大学 Electroencephalogram classification detection device based on lacuna characteristics
EP2986203B1 (en) * 2013-04-14 2022-12-07 Yissum Research Development Company of the Hebrew University of Jerusalem Ltd. Classifying eeg signals in response to visual stimulus
JP6087786B2 (en) * 2013-10-22 2017-03-01 トヨタ自動車株式会社 Voluntary movement identification device
CN103623504A (en) * 2013-12-10 2014-03-12 天津市鸣都科技发展有限公司 Electroencephalo-graph language barrier recovery apparatus
CN104127179B (en) * 2014-04-13 2016-04-06 北京工业大学 The brain electrical feature extracting method of a kind of advantage combination of electrodes and empirical mode decomposition
US10607737B2 (en) 2015-01-20 2020-03-31 Northwestern University Systems and methods to derive models to evaluate behavior outcomes based on brain responses to complex sounds
KR101904431B1 (en) 2016-01-26 2018-10-08 (주)피지오랩 Digital biopotential sensor system
WO2017136656A1 (en) 2016-02-04 2017-08-10 Northwestern University Methods and systems for identifying non-penetrating brain injuries
CN105962889A (en) * 2016-04-13 2016-09-28 王菊 Myasthenia examination device for neurology department
KR101870758B1 (en) * 2016-10-13 2018-06-26 (주)로임시스템 Bio-signal detection apparatus for bio-signal interference identification
WO2018093181A1 (en) * 2016-11-16 2018-05-24 삼성전자 주식회사 Electronic device and control method thereof
KR20180055660A (en) * 2016-11-16 2018-05-25 삼성전자주식회사 Electronic apparatus and control method thereof
CN108078563A (en) * 2017-01-11 2018-05-29 浙江师范大学 A kind of EEG signal analysis method of integrated classifier
GB2560339B (en) 2017-03-07 2020-06-03 Transf Ai Ltd Prediction of cardiac events
JP7336755B2 (en) 2017-07-28 2023-09-01 パナソニックＩｐマネジメント株式会社 DATA GENERATION DEVICE, BIOLOGICAL DATA MEASUREMENT SYSTEM, CLASSIFIER GENERATION DEVICE, DATA GENERATION METHOD, CLASSIFIER GENERATION METHOD, AND PROGRAM
JP7069716B2 (en) 2017-12-28 2022-05-18 株式会社リコー Biological function measurement and analysis system, biological function measurement and analysis program, and biological function measurement and analysis method
US11596380B2 (en) 2019-02-15 2023-03-07 Novasignal Corp. Categorization of waveform morphologies
CN110811548A (en) * 2019-10-09 2020-02-21 深圳大学 Memory state evaluation method, system, device and storage medium
CN113384277B (en) * 2020-02-26 2022-09-20 京东方科技集团股份有限公司 Electrocardiogram data classification method and classification system
CN111466909A (en) * 2020-04-14 2020-07-31 清华大学 Target detection method and system based on electroencephalogram characteristics
CN112036229B (en) * 2020-06-24 2024-04-19 宿州小马电子商务有限公司 Intelligent bassinet electroencephalogram signal channel configuration method with demand sensing function
CN112022140B (en) * 2020-07-03 2023-02-17 上海数创医疗科技有限公司 Automatic diagnosis method and system for diagnosis conclusion of electrocardiogram
CN114298230A (en) * 2021-12-29 2022-04-08 福州大学 Lower limb movement identification method and system based on surface electromyographic signals

Family Cites Families (3)

* Cited by examiner, † Cited by third party
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US4860216A (en) * 1986-11-13 1989-08-22 The United States Of America As Represented By The Secretary Of The Air Force Communication adaptive multi-sensor system
US5458117A (en) * 1991-10-25 1995-10-17 Aspect Medical Systems, Inc. Cerebral biopotential analysis system and method
US5661666A (en) * 1992-11-06 1997-08-26 The United States Of America As Represented By The Secretary Of The Navy Constant false probability data fusion system

Also Published As

Publication number Publication date
AU2005279954B2 (en) 2010-09-09
JP2008517636A (en) 2008-05-29
EP1789907B1 (en) 2010-04-14
AU2005279954A1 (en) 2006-03-09
ATE464616T1 (en) 2010-04-15
EP1789907A1 (en) 2007-05-30
WO2006026548A1 (en) 2006-03-09

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